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pro vyhledávání: '"Khorasani, Sadegh"'
Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance. In recent years, several variance-reduced PG methods have been proposed with a theoretical guarantee of converging to an approximate first-orde
Externí odkaz:
http://arxiv.org/abs/2311.08914
Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-grad
Externí odkaz:
http://arxiv.org/abs/2205.08253
Autor:
Bahari, Mohammadhossein, Zehtab, Vahid, Khorasani, Sadegh, Ayromlou, Sana, Saadatnejad, Saeed, Alahi, Alexandre
Anticipating motions of vehicles in a scene is an essential problem for safe autonomous driving systems. To this end, the comprehension of the scene's infrastructure is often the main clue for predicting future trajectories. Most of the proposed appr
Externí odkaz:
http://arxiv.org/abs/2110.03706
Akademický článek
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